Randomized clinical trials--removing unnecessary obstacles

Synthetic data as an investigative tool in hypertension and renal diseases research

There is a growing body of clinical research on the utility of synthetic data derivatives, an emerging research tool in medicine. In nephrology, clinicians can use machine learning and artificial intelligence as powerful aids in their clinical decision-making while also preserving patient privacy. This is especially important given the epidemiology of chronic kidney disease, renal oncology, and hypertension worldwide. However, there remains a need to create a framework for guidance regarding how to better utilize synthetic data as a practical application in this research.

Optimizing accrual to a large-scale, clinically integrated randomized trial in anesthesiology: A 2-year analysis of recruitment

BACKGROUND

Performing large randomized trials in anesthesiology is often challenging and costly. The clinically integrated randomized trial is characterized by simplified logistics embedded into routine clinical practice, enabling ease and efficiency of recruitment, offering an opportunity for clinicians to conduct large, high-quality randomized trials under low cost. Our aims were to (1) demonstrate the feasibility of the clinically integrated trial design in a high-volume anesthesiology practice and (2) assess whether trial quality improvement interventions led to more balanced accrual among study arms and improved trial compliance over time.

METHODS

This is an interim analysis of recruitment to a cluster-randomized trial investigating three nerve block approaches for mastectomy with immediate implant-based reconstruction: paravertebral block (arm 1), paravertebral plus interpectoral plane blocks (arm 2), and serratus anterior plane plus interpectoral plane blocks (arm 3). We monitored accrual and consent rates, clinician compliance with the randomized treatment, and availability of outcome data. Assessment after the initial year of implementation showed a slight imbalance in study arms suggesting areas for improvement in trial compliance. Specific improvement interventions included increasing the frequency of communication with the consenting staff and providing direct feedback to clinician investigators about their individual recruitment patterns. We assessed overall accrual rates and tested for differences in accrual, consent, and compliance rates pre- and post-improvement interventions.

RESULTS

Overall recruitment was extremely high, accruing close to 90% of the eligible population. In the pre-intervention period, there was evidence of bias in the proportion of patients being accrued and receiving the monthly block, with higher rates in arm 3 (90%) compared to arms 1 (81%) and 2 (79%, p = 0.021). In contrast, in the post-intervention period, there was no statistically significant difference between groups (p = 0.8). Eligible for randomization rate increased from 89% in the pre-intervention period to 95% in the post-intervention period (difference 5.7%; 95% confidence interval = 2.2%-9.4%, p = 0.002). Consent rate increased from 95% to 98% (difference of 3.7%; 95% confidence interval = 1.1%-6.3%; p = 0.004). Compliance with the randomized nerve block approach was maintained at close to 100% and availability of primary outcome data was 100%.

CONCLUSION

The clinically integrated randomized trial design enables rapid trial accrual with a high participant compliance rate in a high-volume anesthesiology practice. Continuous monitoring of accrual, consent, and compliance rates is necessary to maintain and improve trial conduct and reduce potential biases. This trial methodology serves as a template for the implementation of other large, low-cost randomized trials in anesthesiology.

Exploring genetic links between blood metabolites and gout susceptibility

BACKGROUND

Gout, a prevalent form of inflammatory arthritis, has a complex etiology where the causal relationship between metabolites and the disease remains underexplored. This study aims to elucidate the impact of genetically determined blood metabolites on gout.

METHODS

Employing a two-sample bidirectional Mendelian randomization analysis, we examined the association between 1400 blood metabolites and gout. Causal associations were determined using the inverse variance weighted (IVW) method with false discovery rate (FDR) correction. Sensitivity analyses encompassed weighted models, MR-Egger, weighted median, and MR-PRESSO approaches. MR-pleiotropy and Cochran's Q statistic were utilized to evaluate potential heterogeneity and pleiotropy. Additionally, metabolic pathway analyses were conducted to pinpoint relevant pathways.

RESULTS

Of the initial 4 serum metabolites identified, 3 known metabolites-hexanoylglutamine levels, mannose content, and the phosphate to mannose ratio-were found to be causally associated with gout, along with 55 serum metabolites identified as potential predictors of gout (PIVW < 0.05). Furthermore, we discovered 3 metabolic pathways implicated in gouty attacks.

CONCLUSION

Our findings, derived from Mendelian randomization, indicate that the identified metabolites and pathways may serve as biomarkers for clinical screening and prevention of gout. Additionally, they offer novel insights into the mechanisms of the disease and potential drug targets. Key points • Conducted a comprehensive Mendelian randomization study involving 1400 blood metabolites to explore their genetic impact on gout development and progression • Identified three key metabolites-hexanoylglutamine, mannose, and the phosphate-to-mannose ratio-with causal associations to gout, highlighting their potential use as biomarkers for early detection and risk stratification • Discovered 55 additional serum metabolites as potential predictors of gout, offering new insights into the pathophysiology of the disease and identifying high-risk individuals • Revealed three novel metabolic pathways involved in gout attacks, providing new therapeutic targets for precision medicine in gout treatment.

Trial Factors Associated With Completion of Clinical Trials Evaluating AI: Retrospective Case-Control Study

BACKGROUND

Evaluation of artificial intelligence (AI) tools in clinical trials remains the gold standard for translation into clinical settings. However, design factors associated with successful trial completion and the common reasons for trial failure are unknown.

OBJECTIVE

This study aims to compare trial design factors of complete and incomplete clinical trials testing AI tools. We conducted a case-control study of complete (n=485) and incomplete (n=51) clinical trials that evaluated AI as an intervention of ClinicalTrials.gov.

METHODS

Trial design factors, including area of clinical application, intended use population, and intended role of AI, were extracted. Trials that did not evaluate AI as an intervention and active trials were excluded. The assessed trial design factors related to AI interventions included the domain of clinical application related to organ systems; intended use population for patients or health care providers; and the role of AI for different applications in patient-facing clinical workflows, such as diagnosis, screening, and treatment. In addition, we also assessed general trial design factors including study type, allocation, intervention model, masking, age, sex, funder, continent, length of time, sample size, number of enrollment sites, and study start year. The main outcome was the completion of the clinical trial. Odds ratio (OR) and 95% CI values were calculated for all trial design factors using propensity-matched, multivariable logistic regression.

RESULTS

We queried ClinicalTrials.gov on December 23, 2023, using AI keywords to identify complete and incomplete trials testing AI technologies as a primary intervention, yielding 485 complete and 51 incomplete trials for inclusion in this study. Our nested propensity-matched, case-control results suggest that trials conducted in Europe were significantly associated with trial completion when compared with North American trials (OR 2.85, 95% CI 1.14-7.10; P=.03), and the trial sample size was positively associated with trial completion (OR 1.00, 95% CI 1.00-1.00; P=.02).

CONCLUSIONS

Our case-control study is one of the first to identify trial design factors associated with completion of AI trials and catalog study-reported reasons for AI trial failure. We observed that trial design factors positively associated with trial completion include trials conducted in Europe and sample size. Given the promising clinical use of AI tools in health care, our results suggest that future translational research should prioritize addressing the design factors of AI clinical trials associated with trial incompletion and common reasons for study failure.

Good Statistical Monitoring: A Flexible Open-Source Tool to Detect Risks in Clinical Trials

BACKGROUND

Risk-based quality management is a regulatory-recommended approach to manage risk in a clinical trial. A key element of this strategy is to conduct risk-based monitoring to detect potential risks to critical data and processes earlier. However, there are limited publicly available tools to perform the analytics required for this purpose. Good Statistical Monitoring is a new open-source solution developed to help address this need.

METHODS

A team of statisticians, data scientists, clinicians, data managers, clinical operations, regulatory, and quality compliance staff collaborated to design Good Statistical Monitoring, an R package, to flexibly and efficiently implement end-to-end analyses of key risks. The package currently supports the mapping of clinical trial data from a variety of formats, evaluation of 12 key risk indicators, interactive visualization of analysis results, and creation of standardized reports.

RESULTS

The Good Statistical Monitoring package is freely available on GitHub and empowers clinical study teams to proactively monitor key risks. It employs a modular workflow to perform risk assessments that can be customized by replacing any workflow component with a study-specific alternative. Results can be exported to other clinical systems or can be viewed as an interactive report to facilitate follow-up risk mitigation. Rigorous testing and qualification are performed as part of each release to ensure package quality.

CONCLUSIONS

Good Statistical Monitoring is an open-source solution designed to enable clinical study teams to implement statistical monitoring of critical risks, as part of a comprehensive risk-based quality management strategy.

Immune-related adverse events in patients treated with immunotherapy for locally advanced or metastatic NSCLC in real-world settings: a systematic review and meta-analysis

Introduction

Randomized clinical trials (RCTs) represent the mainstay for the approval of new treatments. However, stringent inclusion criteria often cause them to depart from the daily clinical practice. Real-world (RW) evidence have a complementing role, filling the gap between the efficacy of a treatment and its effectiveness. Immune checkpoint inhibitors (ICIs) have changed the treatment scenario for non-small cell lung cancer (NSCLC); immune-related adverse events (irAEs) could become life-threatening events, when not timely managed. We performed a systematic review and meta-analysis on the RW impact of irAEs through the years.

Methods

The systematic review focused on irAEs occurred in locally advanced or metastatic NSCLC patients, treated with ICIs in a RW setting. We queried two electronic databases (Embase and Medline) from 1996 to August 2022. We then conducted a meta-analysis dividing the results in two cohorts (2015-2018 and 2019-2021). We described the prevalence of patients with irAEs of any or severe grade (G). Estimates were expressed as proportions up to the second decimal point (effect size, ES). IrAEs of interest were those involving the skin, the liver, the endocrine system or the gastro-intestinal system.

Results

Overall, 21 RW studies on 5,439 patients were included in the quantitative and qualitative synthesis. The prevalence of G≥3 irAEs was slightly lower in the 2015-2018 subgroup, while the prevalence of irAEs of any grade was similar for both periods. Overall, we observed a higher ES for gastrointestinal, hepatic and lung irAEs, while a lower ES was reported for skin or endocrine irAEs. Endocrine irAEs were reported in 10 out of 21 studies, with a slight increase in the most recent studies, while cutaneous toxicities were mostly reported in two studies lead within the first time-period. Pulmonary, gastrointestinal, and hepatic toxicities, showed a more heterogeneous distribution of ES over time.

Discussion

Our findings showed that the frequency of irAEs remained stable across the two calendar periods examined in our meta-analysis. This finding suggests that RW data might not be able to identify a potential learning curve in detection and management of irAEs.

Generating and using real-world data: A worthwhile uphill battle

The precision oncology paradigm challenges the feasibility and data generalizability of traditional clinical trials. Consequently, an unmet need exists for practical approaches to test many subgroups, evaluate real-world drug value, and gather comprehensive, accessible datasets to validate novel biomarkers. Real-world data (RWD) are increasingly recognized to have the potential to fill this gap in research methodology. Established applications of RWD include informing disease epidemiology, pharmacovigilance, and healthcare quality assessment. Currently, concerns regarding RWD quality and comprehensiveness, privacy, and biases hamper their broader application. Nonetheless, RWD may play a pivotal role in supplementing clinical trials, enabling conditional reimbursement and accelerated drug access, and innovating trial conduct. Moreover, purpose-built RWD repositories may support the extension or refinement of drug indications and facilitate the discovery and validation of new biomarkers. This perspective explores the potential of leveraging RWD to advance oncology, highlights its benefits and challenges, and suggests a path forward in this evolving field.

Assessing the causal effect of genetically predicted metabolites and metabolic pathways on stroke

BACKGROUND

Stroke is a common neurological disorder that disproportionately affects middle-aged and elderly individuals, leading to significant disability and mortality. Recently, human blood metabolites have been discovered to be useful in unraveling the underlying biological mechanisms of neurological disorders. Therefore, we aimed to evaluate the causal relationship between human blood metabolites and susceptibility to stroke.

METHODS

Summary data from genome-wide association studies (GWASs) of serum metabolites and stroke and its subtypes were obtained separately. A total of 486 serum metabolites were used as the exposure. Simultaneously, 11 different stroke phenotypes were set as the outcomes, including any stroke (AS), any ischemic stroke (AIS), large artery stroke (LAS), cardioembolic stroke (CES), small vessel stroke (SVS), lacunar stroke (LS), white matter hyperintensities (WMH), intracerebral hemorrhage (ICH), subarachnoid hemorrhage (SAH), transient ischemic attack (TIA), and brain microbleeds (BMB). A two-sample Mendelian randomization (MR) study was conducted to investigate the causal effects of serum metabolites on stroke and its subtypes. The inverse variance-weighted MR analyses were conducted as causal estimates, accompanied by a series of sensitivity analyses to evaluate the robustness of the results. Furthermore, a reverse MR analysis was conducted to assess the potential for reverse causation. Additionally, metabolic pathway analysis was performed using the web-based MetOrigin.

RESULTS

After correcting for the false discovery rate (FDR), MR analysis results revealed remarkable causative associations with 25 metabolites. Further sensitivity analyses confirmed that only four causative associations involving three specific metabolites passed all sensitivity tests, namely ADpSGEGDFXAEGGGVR* for AS (OR: 1.599, 95% CI 1.283-1.993, p = 2.92 × 10-5) and AIS (OR: 1.776, 95% CI 1.380-2.285, p = 8.05 × 10-6), 1-linoleoylglycerophosph-oethanolamine* for LAS (OR: 0.198, 95% CI 0.091-0.428, p = 3.92 × 10-5), and gamma-glutamylmethionine* for SAH (OR: 3.251, 95% CI 1.876-5.635, p = 2.66 × 10-5), thereby demonstrating a high degree of stability. Moreover, eight causative associations involving seven other metabolites passed both sensitivity tests and were considered robust. The association result of one metabolite (glutamate for LAS) was considered non-robust. As for the remaining metabolites, we speculate that they may potentially possess underlying causal relationships. Notably, no common metabolites emerged from the reverse MR analysis. Moreover, after FDR correction, metabolic pathway analysis identified 40 significant pathways across 11 stroke phenotypes.

CONCLUSIONS

The identified metabolites and their associated metabolic pathways are promising circulating metabolic biomarkers, holding potential for their application in stroke screening and preventive strategies within clinical settings.

Development, implementation, and dissemination of operational innovations across the trial innovation network

Improving the quality and conduct of multi-center clinical trials is essential to the generation of generalizable knowledge about the safety and efficacy of healthcare treatments. Despite significant effort and expense, many clinical trials are unsuccessful. The National Center for Advancing Translational Science launched the Trial Innovation Network to address critical roadblocks in multi-center trials by leveraging existing infrastructure and developing operational innovations. We provide an overview of the roadblocks that led to opportunities for operational innovation, our work to develop, define, and map innovations across the network, and how we implemented and disseminated mature innovations.

Leveraging Artificial Intelligence and Synthetic Data Derivatives for Spine Surgery Research

STUDY DESIGN

Retrospective cohort study.

OBJECTIVES

Leveraging electronic health records (EHRs) for spine surgery research is impeded by concerns regarding patient privacy and data ownership. Synthetic data derivatives may help overcome these limitations. This study's objective was to validate the use of synthetic data for spine surgery research.

METHODS

Data came from the EHR from 15 hospitals. Patients that underwent anterior cervical or posterior lumbar fusion (2010-2020) were included. Real data were obtained from the EHR. Synthetic data was generated to simulate the properties of the real data, without maintaining a one-to-one correspondence with real patients. Within each cohort, ability to predict 30-day readmissions and 30-day complications was evaluated using logistic regression and extreme gradient boosting machines (XGBoost).

RESULTS

We identified 9,072 real and 9,088 synthetic cervical fusion patients. Descriptive characteristics were nearly identical between the 2 datasets. When predicting readmission, models built using real and synthetic data both had c-statistics of .69-.71 using logistic regression and XGBoost. Among 12,111 real and 12,126 synthetic lumbar fusion patients, descriptive characteristics were nearly the same for most variables. Using logistic regression and XGBoost to predict readmission, discrimination was similar with models built using real and synthetic data (c-statistics .66-.69). When predicting complications, models derived using real and synthetic data showed similar discrimination in both cohorts. Despite some differences, the most influential predictors were similar in the real and synthetic datasets.

CONCLUSION

Synthetic data replicate most descriptive and predictive properties of real data, and therefore may expand EHR research in spine surgery.

Pragmatic Comparative Effectiveness Trials and Learning Health Systems in Pain Medicine: Opportunities and Challenges

Large randomized clinical trials or aggregates of clinical trials represent the highest levels of clinical evidence because they minimize different sources of confounding and bias. The current review provides an in-depth discussion of the challenges faced and methods we can use to overcome these obstacles to tailor novel designs of pragmatic effectiveness trials to pain medicine. The authors describe their experiences with an open-source learning health system to collect high-quality evidence and conduct pragmatic clinical trials within a busy academic pain center.

Improving the Approach to Defining, Classifying, Reporting and Monitoring Adverse Events in Seriously Ill Older Adults: Recommendations from a Multi-stakeholder Convening

BACKGROUND

Clinical trials are needed to study topics relevant to older adults with serious illness. Investigators conducting clinical trials with this population are challenged by how to appropriately define, classify, report, and monitor serious and non-serious adverse events (SAEs/AEs), given that some traditionally reported AEs (pressure ulcers, delirium) and SAEs (death, hospitalization) are common in persons with serious illness, and may be consistent with their goals of care.

OBJECTIVES

A multi-stakeholder group convened to establish greater clarity on and new approaches to address this critical issue.

PARTICIPANTS

Thirty-two study investigators, members of regulatory and sponsor agencies, and patient stakeholders took part.

APPROACH

The group met virtually four times and, using a collaborative approach, conducted a survey, select interviews, and reviewed regulatory guidance to collectively define the problem and identify a new approach.

RESULTS

SAE/AE challenges fell into two areas: (1) definitions and classifications, including (a) implausible relationships, (b) misalignment with patient-centered care goals, and (c) well-known associations, and (2) reporting and monitoring, including (a) limited guidance, (b) inconsistent standards across regulators, and (c) Data Safety Monitoring Board (DSMB) member knowledge gaps. Problems largely reflected practice norms rather than regulatory requirements that already support context-specific and aggregate reporting. Approaches can be improved by adopting principles that better align strategies for addressing adverse events with the type of intervention being tested, favoring routine and aggregate over expedited reporting, and prioritizing how SAE/AEs relate to patient-centered care goals. Reporting plans and decisions should follow an algorithm underpinned by these principles.

CONCLUSIONS

Adoption of the proposed approach-and supporting it with education and better alignment with regulatory guidance and procedures-could improve the quality and efficiency of clinical trials' safety involving older adults with serious illness and other vulnerable populations.

Artificial intelligence in spine surgery

The continuous progress of research and clinical trials has offered a wide variety of information concerning the spine and the treatment of the different spinal pathologies that may occur. Planning the best therapy for each patient could be a very difficult and challenging task as it often requires thorough processing of the patient's history and individual characteristics by the clinician. Clinicians and researchers also face problems when it comes to data availability due to patients' personal information protection policies. Artificial intelligence refers to the reproduction of human intelligence via special programs and computers that are trained in a way that simulates human cognitive functions. Artificial intelligence implementations to daily clinical practice such as surgical robots that facilitate spine surgery and reduce radiation dosage to medical staff, special algorithms that can predict the possible outcomes of conservative versus surgical treatment in patients with low back pain and disk herniations, and systems that create artificial populations with great resemblance and similar characteristics to real patients are considered to be a novel breakthrough in modern medicine. To enhance the body of the related literature and inform the readers on the clinical applications of artificial intelligence, we performed this review to discuss the contribution of artificial intelligence in spine surgery and pathology.

A Pragmatic Randomized Feasibility Trial of Influenza Vaccines

BACKGROUND: The relative vaccine effectiveness (rVE) of high-dose quadrivalent influenza vaccines (QIV-HD) versus standard-dose quadrivalent influenza vaccines (QIV-SD) against hospitalizations and mortality in the general older population has not been evaluated in an individually randomized trial. Because of the large sample size required, such a trial will need to incorporate innovative, pragmatic elements. METHODS: We conducted a pragmatic, open-label, active-controlled, randomized feasibility trial in Danish citizens aged 65 to 79 years during the 2021–2022 influenza season. Participants were randomly assigned 1:1 to receive QIV-HD or QIV-SD. Randomization was integrated into routine vaccination practice, and the trial relied solely on nationwide administrative health registries for data collection. Outcomes consisted of a feasibility assessment and descriptive rVE estimates. RESULTS: We invited 34,000 persons to participate. A total of 12,477 randomly assigned participants were included in the final analyses. Mean (±SD) age was 71.7±3.9 years, and 5877 (47.1%) were women. Registry-based data collection was feasible, with complete follow-up data for 99.9% of participants. Baseline characteristics were comparable to those of the overall Danish population aged 65 to 79 years. The incidence of hospitalization for influenza or pneumonia was 10 (0.2%) of 6245 in the QIV-HD group and 28 (0.4%) of 6232 in the QIV-SD group (rVE, 64.4%; 95% confidence interval, 24.4 to 84.6). All-cause death occurred in 21 (0.3%) and 41 (0.7%) participants in the QIV-HD and QIV-SD groups, respectively (rVE, 48.9%; 95% confidence interval, 11.5 to 71.3). CONCLUSIONS: Conducting a pragmatic randomized trial of QIV-HD versus QIV-SD using existing infrastructure and registry-based data collection was feasible. The findings of lower incidence of hospitalization for influenza or pneumonia and all-cause mortality in the QIV-HD group compared with the QIV-SD group require replication in a future, fully powered trial. (Funded by Sanofi; ClinicalTrials.gov number, NCT05048589.)

The effects on clinical trial activity of direct funding and taxation policy interventions made by government: A systematic review

CONTEXT

Governments have attempted to increase clinical trial activity in their jurisdictions using a range of methods including targeted direct funding and industry tax rebates. The effectiveness of the different approaches employed is unclear.

OBJECTIVE

To systematically review the effects of direct government financing interventions by allowing companies to reduce their tax payable on clinical trial activity.

DATA SOURCES

Pub Med, Scopus, Sage, ProQuest, Google Scholar and Google were searched up to the 11th of April 2022. In addition, the reference lists of all potentially eligible documents were hand searched to identify additional reports. Following feedback from co-authors, information on a small number of additional interventions were specifically sought out and included.

DATA EXTRACTION

Summary information about potentially eligible reports were reviewed independently by two researchers, followed by extraction of data into a structured spreadsheet for eligible studies. The primary outcomes of interest were the number of clinical trials and the expenditure on clinical trials but data about other evaluations were also collected.

RESULTS

There were 1694 potentially eligible reports that were reviewed. Full text assessments were done for 304, and 30 reports that provided data on 43 interventions were included- 29 that deployed targeted direct funding and 14 that provided tax rebates or exemptions. There were data describing effects on a primary outcome for 25/41 of the interventions. The most common types of interventions were direct funding to researchers via special granting mechanisms and tax offsets to companies and research organisations. All 25 of the studies for which data were available reported a positive impact on numbers and/or expenditure on clinical trials though the robustness of evaluations was limited for many. Estimates of the magnitude of effects of interventions were reported inconsistently, varied substantially, and could not be synthesised quantitatively, though targeted direct funding interventions appeared to be associated with more immediate impact on clinical trial activity.

CONCLUSION

There is a high likelihood that governments can increase clinical trial activity with either direct or indirect fiscal mechanisms. Direct funding may provide a more immediate and tangible return on investment than tax rebates.

Improved clinical investigation and evaluation of high-risk medical devices: the rationale and objectives of CORE-MD (Coordinating Research and Evidence for Medical Devices)

In the European Union (EU) the delivery of health services is a national responsibility but there are concerted actions between member states to protect public health. Approval of pharmaceutical products is the responsibility of the European Medicines Agency, whereas authorizing the placing on the market of medical devices is decentralized to independent 'conformity assessment' organizations called notified bodies. The first legal basis for an EU system of evaluating medical devices and approving their market access was the medical device directives, from the 1990s. Uncertainties about clinical evidence requirements, among other reasons, led to the EU Medical Device Regulation (2017/745) that has applied since May 2021. It provides general principles for clinical investigations but few methodological details-which challenges responsible authorities to set appropriate balances between regulation and innovation, pre- and post-market studies, and clinical trials and real-world evidence. Scientific experts should advise on methods and standards for assessing and approving new high-risk devices, and safety, efficacy, and transparency of evidence should be paramount. The European Commission recently awarded a Horizon 2020 grant to a consortium led by the European Society of Cardiology and the European Federation of National Associations of Orthopaedics and Traumatology, that will review methodologies of clinical investigations, advise on study designs, and develop recommendations for aggregating clinical data from registries and other real-world sources. The CORE-MD project (Coordinating Research and Evidence for Medical Devices) will run until March 2024; here we describe how it may contribute to the development of regulatory science in Europe.

Research Aims in Clinical Medicine: Description, Identification, or Explanation

Biomedical research can generally be categorized into 1 of 3 aims: describing the occurrence of disease; identifying persons with or at increased risk of disease including diagnostic and prognostic studies; and explaining the occurrence of disease including etiologic and efficacy studies.

Activation of Investigator-Initiated Clinical Trials with a Pharmaceutical for Cancer Patients before and after Post-Millennial Changes of Regulations in Germany and Europe

Simple Summary

This opinion paper describes the regulatory hurdles for a clinical oncologist and physician scientist to activate an Investigator-Initiated Trial (IIT) before and after 2004 with German regulation as an example. Changes in legal framework with impacts on time and costs to activate a clinical trial are described. Evidence needed to reach the objective of higher patient safety and trial quality by European Union (EU) Clinical Trial Directive (CTD) 2001/20 is discussed.

Abstract

Shortly after the beginning of the year 2000, multiple legal changes with impacts on the regulatory framework of clinical trials became effective almost simultaneously. They included the European Union (EU) Clinical Trial Directive (CTD) 2001/20 followed by major changes in national drug laws, the change in the legal status of German University Hospitals (1998), and a new disease-related groups (DRG)-based reimbursement system for hospitals in Germany (2000). Together, these changes created enormous bureaucratic and financial inhibition of activation and conduct of academic investigator-initiated clinical trials (IIT). Examples for activating clinical trials in oncology before and after 2004 are outlined and discussed, focussing on extended time frames, the establishment of centralized responsibility structures and the exploding financial consequences. In addition, the evolution of trial numbers and the distribution of trial initiators between “commercial” and “academic” over time are discussed together with the occurrence of clinical registries. At the same time, progress in molecular biology led to a plethora of new targets for effective pharmacological therapy of life-threatening diseases such as cancer, and the overall number of clinical trials has not decreased. Yet, judging the regulatory and administrative hurdles between scientific study design and first-patient on trial before and after 2004 and weighing these against the lack of evidence that this regulation has achieved its goal to enhance patient safety and trial quality, the necessity to completely overhaul this CTD becomes obvious. A main goal of such an initiative should be to minimize bureaucracy. For the specific situation in Germany, relocation of responsibility and freedom to operate in University Hospitals and Medical Faculties back to the physician–scientists and reduction in interference by legal divisions should be a goal as well as increasing the public financial support for IITs.

Defining clinical trial quality from the perspective of resource-limited settings: A qualitative study based on interviews with investigators, sponsors, and monitors conducting clinical trials in sub-Saharan Africa

BACKGROUND

Increasing clinical trial cost and complexity, as well as a high waste of clinical trial investment over the past decades, have changed the way clinical trial quality is managed. Recent evidence has highlighted that the lack of a clear clinical trial quality definition may have contributed to previous inefficiencies. This study aims to support the understanding of what clinical trial quality entails from the perspective of resource-limited settings.

METHODOLOGY/PRINCIPAL FINDINGS

We conducted 46 semi-structured interviews involving investigators, sponsors, and monitors with experience in conducting clinical trials in 27 countries in sub-Saharan Africa. The questionnaire addressed the overall meaning of clinical trial quality and a conclusive clinical trial quality definition, as well as specific aspects of resource-limited settings across the clinical trial process. We held the interviews either in person, via Skype or by phone. They were recorded and transcribed verbatim, and we performed the analysis using The Framework Method. The analysis of clinical trial quality definitions resulted in 11 elements, which were summarised into a clinical trial quality concept consisting of two components: 1) clinical trial quality building factors (Scientific factors and Moral factors) and 2) promoting factors (Context adaptation; Infrastructure; Partnership; Operational excellence; Quality system). 12 resource-limited settings specific themes were identified. These themes were all categorised under the promoting factors "Context adaptation", "Infrastructure", and "Partnership".

CONCLUSIONS/SIGNIFICANCE

We found that in order to enable comprehensive clinical trial quality management, clinical trial quality should be defined by a multidimensional concept that includes not only scientific and ethical, but also quality-promoting factors. Such a concept is of general relevance and not limited to clinical trials in resource-limited settings, where it naturally carries particular weight. In addition, from the perspective of sub-Saharan Africa, we identified specific categories that appear to be critical for the conduct of clinical trials in resource-limited settings, and we propose respective changes to a particular existing clinical trial quality framework (i.e., INQUIRE).

Early Death and Survival of Patients With Acute Promyelocytic Leukemia in ATRA Plus Arsenic Era: A Population-Based Study

Most randomized trials for acute promyelocytic leukemia (APL) have investigated highly selected patients under idealized conditions, and the findings need to be validated in the real world. We conducted a population-based study of all APL patients in Zhejiang Province, China, with a total population of 82 million people, to assess the generalization of all-trans retinoic acid (ATRA) and arsenic as front-line treatment. The outcomes of APL patients were also analyzed. Between January 2015 and December 2019, 1,233 eligible patients were included in the final analysis. The rate of ATRA and arsenic as front-line treatment increased steadily from 66.2% in 2015 to 83.3% in 2019, with no difference among the size of the center (≥5 or <5 patients per year, p = 0.12) or age (≥60 or <60 years, p = 0.35). The early death (ED) rate, defined as death within 30 days after diagnosis, was 8.2%, and the 3-year overall survival (OS) was 87.9% in the whole patient population. Age (≥60 years) and white blood cell count (>10 × 10⁹/L) were independent risk factors for ED and OS in the multivariate analysis. This population-based study showed that ATRA and arsenic as front-line treatment are widely used under real-world conditions and yield a low ED rate and a high survival rate, which mimic the results from clinical trials, thereby supporting the wider application of APL guidelines in the future.

Improved clinical investigation and evaluation of high-risk medical devices: the rationale and objectives of CORE-MD (Coordinating Research and Evidence for Medical Devices)

In the European Union (EU), the delivery of health services is a national responsibility but there are concerted actions between member states to protect public health. Approval of pharmaceutical products is the responsibility of the European Medicines Agency, while authorising the placing on the market of medical devices is decentralised to independent 'conformity assessment' organisations called notified bodies. The first legal basis for an EU system of evaluating medical devices and approving their market access was the Medical Device Directive, from the 1990s. Uncertainties about clinical evidence requirements, among other reasons, led to the EU Medical Device Regulation (2017/745) that has applied since May 2021. It provides general principles for clinical investigations but few methodological details - which challenges responsible authorities to set appropriate balances between regulation and innovation, pre- and post-market studies, and clinical trials and real-world evidence. Scientific experts should advise on methods and standards for assessing and approving new high-risk devices, and safety, efficacy, and transparency of evidence should be paramount. The European Commission recently awarded a Horizon 2020 grant to a consortium led by the European Society of Cardiology and the European Federation of National Associations of Orthopaedics and Traumatology, that will review methodologies of clinical investigations, advise on study designs, and develop recommendations for aggregating clinical data from registries and other real-world sources. The CORE-MD project (Coordinating Research and Evidence for Medical Devices) will run until March 2024. Here, we describe how it may contribute to the development of regulatory science in Europe. Cite this article: EFORT Open Rev 2021;6:839-849. DOI: 10.1302/2058-5241.6.210081.

Evolution of Evidence-Based Medicine in Stroke

The introduction and evolution of evidence-based stroke medicine has realized major advances in our knowledge about stroke, methods of medical research, and patient outcomes that continue to complement traditional individual patient care. It is humbling to recall the state of knowledge and scientific endeavour of our forebears who were unaware of what we know now and yet pursued the highest standards for evaluating and delivering effective stroke care. The science of stroke medicine has evolved from pathophysiological theory to empirical testing. Progress has been steady, despite inevitable disappointments and cul-de-sacs, and has occasionally been punctuated by sensational breakthroughs, such as the advent of reperfusion therapies guided by imaging.

Can Administrative Health Data Improve the Gold Standard? Evidence from a Model of the Progression of Myocardial Infarction

Background: Myocardial infarction (MI), remains one of the leading causes of death and disability globally but publications on the progression of MI using data from the real world are limited. Multistate models have been widely used to estimate transition rates between disease states to evaluate the cost-effectiveness of healthcare interventions. We apply a Bayesian multistate hidden Markov model to investigate the progression of MI using a longitudinal dataset from Queensland, Australia. Objective: To apply a new model to investigate the progression of myocardial infarction (MI) and to show the potential to use administrative data for economic evaluation and modeling disease progression. Methods: The cohort includes 135,399 patients admitted to public hospitals in Queensland, Australia, in 2010 treatment of cardiovascular diseases. Any subsequent hospitalizations of these patients were followed until 2015. This study focused on the sub-cohort of 8705 patients hospitalized for MI. We apply a Bayesian multistate hidden Markov model to estimate transition rates between health states of MI patients and adjust for delayed enrolment biases and misclassification errors. We also estimate the association between age, sex, and ethnicity with the progression of MI. Results: On average, the risk of developing Non-ST segment elevation myocardial infarction (NSTEMI) was 8.7%, and ST-segment elevation myocardial infarction (STEMI) was 4.3%. The risk varied with age, sex, and ethnicity. The progression rates to STEMI or NSTEMI were higher among males, Indigenous, or elderly patients. For example, the risk of STEMI among males was 4.35%, while the corresponding figure for females was 3.71%. After adjustment for misclassification, the probability of STEMI increased by 1.2%, while NSTEMI increased by 1.4%. Conclusions: This study shows that administrative health data were useful to estimate factors determining the risk of MI and the progression of this health condition. It also shows that misclassification may cause the incidence of MI to be under-estimated.

Making trials part of good clinical care: lessons from the RECOVERY trial

When COVID-19 hit the UK in early 2020, there were no known treatments for a condition that results in the death of around one in four patients hospitalised with this disease. Around the world, possible treatments were administered to huge numbers of patients, without any reliable assessments of safety and efficacy. The rapid generation of high-quality evidence was vital. RECOVERY is a streamlined, pragmatic, randomised controlled trial, which was set up in response to this challenge. As of April 2021, over 39,000 patients have been enrolled from 178 hospital sites in the UK. Within 100 days of its initiation, RECOVERY demonstrated that dexamethasone improves survival for patients with severe disease; a result that was rapidly implemented in the UK and internationally saving hundreds of thousands of lives. Importantly, it also showed that other widely used treatments (such as hydroxychloroquine and azithromycin) have no meaningful benefit for hospitalised patients. This was only possible through randomisation of large numbers of patients and the adoption of streamlined and pragmatic procedures focused on quality, together with widespread collaboration focused on a single goal. RECOVERY illustrates how clinical trials and healthcare can be integrated, even in a pandemic. This approach provides new opportunities to generate the evidence needed for high-quality healthcare not only for a pandemic but for the many other conditions that place a burden on patients and the healthcare system.

Dynamic methods for ongoing assessment of site-level risk in risk-based monitoring of clinical trials: A scoping review

BACKGROUND/AIMS

It is increasingly recognised that reliance on frequent site visits for monitoring clinical trials is inefficient. Regulators and trialists have recently encouraged more risk-based monitoring. Risk assessment should take place before a trial begins to define the overarching monitoring strategy. It can also be done on an ongoing basis, to target sites for monitoring activity. Various methods have been proposed for such prioritisation, often using terms like 'central statistical monitoring', 'triggered monitoring' or, as in the International Conference on Harmonization Good Clinical Practice guidance, 'targeted on-site monitoring'. We conducted a scoping review to identify such methods, to establish if any were supported by adequate evidence to allow wider implementation, and to guide future developments in this field of research.

METHODS

We used seven publication databases, two sets of methodological conference abstracts and an Internet search engine to identify methods for using centrally held trial data to assess site conduct during a trial. We included only reports in English, and excluded reports published before 1996 or not directly relevant to our research question. We used reference and citation searches to find additional relevant reports. We extracted data using a predefined template. We contacted authors to request additional information about included reports.

RESULTS

We included 30 reports in our final dataset, of which 21 were peer-reviewed publications. In all, 20 reports described central statistical monitoring methods (of which 7 focussed on detection of fraud or misconduct) and 9 described triggered monitoring methods; 21 reports included some assessment of their methods' effectiveness, typically exploring the methods' characteristics using real trial data without known integrity issues. Of the 21 with some effectiveness assessment, most contained limited information about whether or not concerns identified through central monitoring constituted meaningful problems. Several reports demonstrated good classification ability based on more than one classification statistic, but never without caveats of unclear reporting or other classification statistics being low or unavailable. Some reports commented on cost savings from reduced on-site monitoring, but none gave detailed costings for the development and maintenance of central monitoring methods themselves.

CONCLUSION

Our review identified various proposed methods, some of which could be combined within the same trial. The apparent emphasis on fraud detection may not be proportionate in all trial settings. Despite some promising evidence and some self-justifying benefits for data cleaning activity, many proposed methods have limitations that may currently prevent their routine use for targeting trial monitoring activity. The implementation costs, or uncertainty about these, may also be a barrier. We make recommendations for how the evidence-base supporting these methods could be improved.

Time-consuming and expensive data quality monitoring procedures persist in clinical trials: A national survey

INTRODUCTION

The Good Clinical Practice guideline identifies that data monitoring is an essential research activity. However, limited evidence exists on how to perform monitoring including the amount or frequency that is needed to ensure data quality. This study aims to explore the monitoring procedures that are implemented to ensure data quality in Australian clinical research studies.

MATERIAL AND METHODS

Clinical studies listed on the Australian and New Zealand Clinical Trials Registry were invited to participate in a national cross-sectional, mixed-mode, multi-contact (postal letter and e-mail) web-based survey. Information was gathered about the types of data quality monitoring procedures being implemented.

RESULTS

Of the 3689 clinical studies contacted, 589 (16.0%) responded, of which 441 (77.4%) completed the survey. Over half (55%) of the studies applied source data verification (SDV) compared to risk-based targeted and triggered monitoring (10-11%). Conducting 100% on-site monitoring was most common for those who implemented the traditional approach. Respondents who did not conduct 100% monitoring, included 1-25% of data points for SDV, centralized or on-site monitoring. The incidence of adverse events and protocol deviations were the most likely factors to trigger a site visit for risk-based triggered (63% and 44%) and centralized monitoring (48% and 44%), respectively.

CONCLUSION

Instead of using more optimal risk-based approaches, small single-site clinical studies are conducting traditional monitoring procedures which are time consuming and expensive. Formal guidelines need to be improved and provided to all researchers for 'new' risk-based monitoring approaches.

Statistical Considerations for Trials in Adjuvant Treatment of Colorectal Cancer

The design of the best possible clinical trials of adjuvant interventions in colorectal cancer will entail the use of both time-tested and novel methods that allow efficient, reliable and patient-relevant therapeutic development. The ultimate goal of this endeavor is to safely and expeditiously bring to clinical practice novel interventions that impact patient lives. In this paper, we discuss statistical aspects and provide suggestions to optimize trial design, data collection, study implementation, and the use of predictive biomarkers and endpoints in phase 3 trials of systemic adjuvant therapy. We also discuss the issues of collaboration and patient centricity, expecting that several novel agents with activity in the (neo)adjuvant therapy of colon and rectal cancers will become available in the near future.

The impact of the global COVID-19 pandemic on the conduct of clinical trials: Return to normalcy by considering the practical impact of a structured ethical analysis

During the outbreak of the COVID-19 pandemic many clinical trials were abruptly halted. Measures to contain the pandemic are currently taking effect and societies in general and healthcare systems in particular are considering how to return to normalcy. This opens up the discussion when and how clinical trials should be restarted while the COVID-19 pandemic has not yet resolved, and what should happen in case of a resurgence of the virus in the coming months. This article uses the four ethical principles framework as a structured approach to come to a set of practical, ethically grounded guidelines for halting and relaunching clinical trials during the COVID-19 pandemic. The framework applied provides a structured approach for all clinical trials stakeholders and thereby prevents unclear reasoning in a complex situation. While it is essential to prevent the virus from resurging and focus on developing a COVID-19 treatment as soon as possible, it is just as important to our society that we continue developing new drugs for other conditions. In this article we argue that the situation for clinical trials is not essentially different from the pre-COVID-19 era and that an overcautious approach will have negative consequences.

Understanding the use of observational and randomized data in cardiovascular medicine

The availability of large datasets from multiple sources [e.g. registries, biobanks, electronic health records (EHRs), claims or billing databases, implantable devices, wearable sensors, and mobile apps], coupled with advances in computing and analytic technologies, have provided new opportunities for conducting innovative health research. Equally, improved digital access to health information has facilitated the conduct of efficient randomized controlled trials (RCTs) upon which clinical management decisions can be based, for instance, by permitting the identification of eligible patients for recruitment and/or linkage for follow-up via their EHRs. Given these advances in cardiovascular data science and the complexities they behold, it is important that health professionals have clarity on the appropriate use and interpretation of observational, so-called 'real-world', and randomized data in cardiovascular medicine. The Cardiovascular Roundtable of the European Society of Cardiology (ESC) held a workshop to explore the future of RCTs and the current and emerging opportunities for gathering and exploiting complex observational datasets in cardiovascular research. The aim of this article is to provide a perspective on the appropriate use of randomized and observational data and to outline the ESC plans for supporting the collection and availability of clinical data to monitor and improve the quality of care of patients with cardiovascular disease in Europe and provide an infrastructure for undertaking pragmatic RCTs. Moreover, the ESC continues to campaign for greater engagement amongst regulators, industry, patients, and health professionals in the development and application of a more efficient regulatory framework that is able to take maximal advantage of new opportunities for improving the design and efficiency of observational studies and RCT in patients with cardiovascular disease.

Central statistical monitoring of investigator-led clinical trials in oncology

Investigator-led clinical trials are pragmatic trials that aim to investigate the benefits and harms of treatments in routine clinical practice. These much-needed trials represent the majority of all trials currently conducted. They are however threatened by the rising costs of clinical research, which are in part due to extensive trial monitoring processes that focus on unimportant details. Risk-based quality management focuses, instead, on “things that really matter”. We discuss the role of central statistical monitoring as part of risk-based quality management. We describe the principles of central statistical monitoring, provide examples of its use, and argue that it could help drive down the cost of randomized clinical trials, especially investigator-led trials, whilst improving their quality.

Generating comparative evidence on new drugs and devices after approval

Certain limitations of evidence available on drugs and devices at the time of market approval often persist in the post-marketing period. Often, post-marketing research landscape is fragmented. When regulatory agencies require pharmaceutical and device manufacturers to conduct studies in the post-marketing period, these studies might remain incomplete many years after approval. Even when completed, many post-marketing studies lack meaningful active comparators, have observational designs, and might not collect patient-relevant outcomes. Regulators, in collaboration with the industry and patients, ought to ensure that the key questions unanswered at the time of drug and device approval are resolved in a timely fashion during the post-marketing phase. We propose a set of seven key guiding principles that we believe will provide the necessary incentives for pharmaceutical and device manufacturers to generate comparative data in the post-marketing period. First, regulators (for drugs and devices), notified bodies (for devices in Europe), health technology assessment organisations, and payers should develop customised evidence generation plans, ensuring that future post-approval studies address any limitations of the data available at the time of market entry impacting the benefit-risk profiles of drugs and devices. Second, post-marketing studies should be designed hierarchically: priority should be given to efforts aimed at evaluating a product's net clinical benefit in randomised trials compared with current known effective therapy, whenever possible, to address common decisional dilemmas. Third, post-marketing studies should incorporate active comparators as appropriate. Fourth, use of non-randomised studies for the evaluation of clinical benefit in the post-marketing period should be limited to instances when the magnitude of effect is deemed to be large or when it is possible to reasonably infer the comparative benefits or risks in settings, in which doing a randomised trial is not feasible. Fifth, efficiency of randomised trials should be improved by streamlining patient recruitment and data collection through innovative design elements. Sixth, governments should directly support and facilitate the production of comparative post-marketing data by investing in the development of collaborative research networks and data systems that reduce the complexity, cost, and waste of rigorous post-marketing research efforts. Last, financial incentives and penalties should be developed or more actively reinforced.

Generating comparative evidence on new drugs and devices before approval

Fewer than half of new drugs have data on their comparative benefits and harms against existing treatment options at the time of regulatory approval in Europe and the USA. Even when active-comparator trials exist, they might not produce meaningful data to inform decisions in clinical practice and health policy. The uncertainty associated with the paucity of well designed active-comparator trials has been compounded by legal and regulatory changes in Europe and the USA that have created a complex mix of expedited programmes aimed at facilitating faster access to new drugs. Comparative evidence generation is even sparser for medical devices. Some have argued that the current process for regulatory approval needs to generate more evidence that is useful for patients, clinicians, and payers in health-care systems. We propose a set of five key principles relevant to the European Medicines Agency, European medical device regulatory agencies, US Food and Drug Administration, as well as payers, that we believe will provide the necessary incentives for pharmaceutical and device companies to generate comparative data on drugs and devices and assure timely availability of evidence that is useful for decision making. First, labelling should routinely inform patients and clinicians whether comparative data exist on new products. Second, regulators should be more selective in their use of programmes that facilitate drug and device approvals on the basis of incomplete benefit and harm data. Third, regulators should encourage the conduct of randomised trials with active comparators. Fourth, regulators should use prospectively designed network meta-analyses based on existing and future randomised trials. Last, payers should use their policy levers and negotiating power to incentivise the generation of comparative evidence on new and existing drugs and devices, for example, by explicitly considering proven added benefit in pricing and payment decisions.

Novel Clinical Trial Designs to Improve the Efficiency of Research

SUMMARY

Large randomized trials provide the highest level of clinical evidence. However, enrolling large numbers of randomized patients across numerous study sites is expensive and often takes years. There will never be enough conventional clinical trials to address the important questions in medicine. Efficient alternatives to conventional randomized trials that preserve protections against bias and confounding are thus of considerable interest. A common feature of novel trial designs is that they are pragmatic and facilitate enrollment of large numbers of patients at modest cost. This article presents trial designs including cluster designs, real-time automated enrollment, and practitioner-preference approaches. Then various adaptive designs that improve trial efficiency are presented. And finally, the article discusses the advantages of embedding randomized trials within registries.

Low-risk trials for children and pregnant women threatened by unnecessary strict regulations. Does the coming EU Clinical Trial Regulation offer a solution?

Investigator-initiated clinical trials are crucial for improving quality of care for children and pregnant women as they are often excluded from industry-initiated trials. However, trials have become increasingly time-consuming and costly since the EU Clinical Trial Directive entered into force in 2001. This directive made compliance with ICH-Good Clinical Practice Guidelines (ethical and quality standard for conducting human subject research) mandatory for all clinical trials, regardless of its risk-classification. By discussing two investigator-initiated, 'low-risk' drug trials, we aim to illustrate that compliance with all GCP requirements makes trials very laborious and expensive, while a clear rationale is missing. This discourages clinical researchers to start and carry out investigator-initiated research. However, the forthcoming EU Clinical Trial Regulation (No 536/2014) seems to provide a solution as it allows for less stringent rules for low-risk trials. We want to raise awareness for these developments in both the clinical research community and the European and national regulatory authorities. Implementation of this forthcoming Regulation regulatory policies should be done in such a way that investigator-initiated trials evaluating standard care interventions will become more feasible. This will allow us to obtain evidence on optimal and safe treatments, especially for groups that are underrepresented in medical research. What is Known • Investigator-initiated trials are indispensable for improving care for children and pregnant women as they are often excluded from industry-initiated trials • Trials have become increasingly time-consuming and costly because of mandatory compliance with ICH-GCP guidelines What is New • The forthcoming EU Clinical Trial Regulation allows less stringent rules for low-risk trials • The national legislator and regulatory authorities should recognize the importance of this opportunity and implement the Regulation in such a way that investigator-initiated trials will become more feasible.

Creating an academic research organization to efficiently design, conduct, coordinate, and analyze clinical trials: The Center for Clinical Trials & Data Coordination

When properly executed, the randomized controlled trial is one of the best vehicles for assessing the effectiveness of one or more interventions. However, numerous challenges may emerge in the areas of study startup, recruitment, data quality, cost, and reporting of results. The use of well-run coordinating centers could help prevent these issues, but very little exists in the literature describing their creation or the guiding principles behind their inception. The Center for Clinical Trials & Data Coordination (CCDC) was established in 2015 through institutional funds with the intent of 1) providing relevant expertise in clinical trial design, conduct, coordination, and analysis; 2) advancing the careers of clinical investigators and CCDC-affiliated faculty; and 3) obtaining large data coordinating center (DCC) grants. We describe the organizational structure of the CCDC as well as the homegrown clinical trial management system integrating nine crucial elements: electronic data capture, eligibility and randomization, drug and external data tracking, safety reporting, outcome adjudication, data and safety monitoring, statistical analysis and reporting, data sharing, and regulatory compliance. Lastly, we share numerous lessons that can be taken from our experience. Specifically, we focus on 1) funding for DCCs, 2) the importance of DCCs to clinical researchers, 3) the expertise of DCC personnel, and 4) continually striving to improve. In conclusion, the CCDC strives to provide high-quality support for the design, conduct, coordination, and analyses of clinical trials, and we hope this paper will serve as a blueprint for future clinical trialists involved in DCCs.

Serious Adverse Effects of Extended-release Niacin/Laropiprant: Results From the Heart Protection Study 2-Treatment of HDL to Reduce the Incidence of Vascular Events (HPS2-THRIVE) Trial

PURPOSE

The Heart Protection Study 2-Treatment of HDL to Reduce the Incidence of Vascular Events (HPS2-THRIVE) trial of patients at high risk of vascular disease found that adding extended-release niacin-laropiprant to intensive statin-based LDL-lowering therapy had no benefit on cardiovascular outcomes. However, the trial also identified previously unrecognized serious adverse effects (including new-onset diabetes, bleeding, and infection). Our objective was to explore the safety profile of niacin-laropiprant and examine whether any patients were at lower (or higher) risk of its adverse effects.

METHODS

HPS2-THRIVE was a randomized, double-blind trial of niacin-laropiprant (2000/40 mg/d) versus placebo among 25,673 patients at high risk of vascular disease. Information on all serious adverse events was collected during a median of 3.9 years of study treatment. Effects of niacin-laropiprant on new-onset diabetes, disturbances of diabetes control, bleeding, infection, and gastrointestinal upset were estimated by (1) time after randomization, (2) severity, (3) baseline characteristics, (4) baseline risk of the adverse event of interest, and (5) risk of major vascular event.

FINDINGS

The hazard ratio (HR) for new-onset diabetes with niacin/laropiprant was 1.32 (95% CI, 1.16-1.51; P < .001), which corresponded to an absolute excess of 4 people (95% CI, 2-6) developing diabetes per 1000 person-years in the study population as a whole. Among the 8299 participants with diabetes at baseline, the HR for serious disturbances in diabetes control was 1.56 (95% CI, 1.35-1.80), corresponding to an absolute excess of 12 (95% CI, 8-16) per 1000 person-years. The HR was 1.38 (95% CI, 1.17-1.63; P < .001) for serious bleeding, corresponding to an absolute excess of 2 (95% CI, 1-3) per 1000 person-years and 1.22 (95% CI, 1.11-1.34; P < .001) for serious infection, corresponding to an absolute excess of 4 (95% CI, 2-6) per 1000 person-years. The excess risks of these serious adverse events were larger in the first year after starting niacin-laropiprant therapy than in later years (except for the excess of infection, which did not appear to attenuate with time), and the risks of nonfatal and fatal events were similarly increased. The absolute excesses of each of these adverse effects were similar regardless of the baseline risk of the outcome.

IMPLICATIONS

Practitioners or patients considering the use of niacin (in addition to, or instead of, a statin) despite the lack of evidence of cardiovascular benefits (at least when added to effective statin therapy) should take account of the significant risks of these serious adverse effects when making such decisions. ClinicalTrials.gov identifier: NCT00461630.

Detection of atypical data in multicenter clinical trials using unsupervised statistical monitoring

BACKGROUND/AIMS

A risk-based approach to clinical research may include a central statistical assessment of data quality. We investigated the operating characteristics of unsupervised statistical monitoring aimed at detecting atypical data in multicenter experiments. The approach is premised on the assumption that, save for random fluctuations and natural variations, data coming from all centers should be comparable and statistically consistent. Unsupervised statistical monitoring consists of performing as many statistical tests as possible on all trial data, in order to detect centers whose data are inconsistent with data from other centers.

METHODS

We conducted simulations using data from a large multicenter trial conducted in Japan for patients with advanced gastric cancer. The actual trial data were contaminated in computer simulations for varying percentages of centers, percentages of patients modified within each center and numbers and types of modified variables. The unsupervised statistical monitoring software was run by a blinded team on the contaminated data sets, with the purpose of detecting the centers with contaminated data. The operating characteristics (sensitivity, specificity and Youden's J-index) were calculated for three detection methods: one using the p-values of individual statistical tests after adjustment for multiplicity, one using a summary of all p-values for a given center, called the Data Inconsistency Score, and one using both of these methods.

RESULTS

The operating characteristics of the three methods were satisfactory in situations of data contamination likely to occur in practice, specifically when a single or a few centers were contaminated. As expected, the sensitivity increased for increasing proportions of patients and increasing numbers of variables contaminated. The three methods showed a specificity better than 93% in all scenarios of contamination. The method based on the Data Inconsistency Score and individual p-values adjusted for multiplicity generally had slightly higher sensitivity at the expense of a slightly lower specificity.

CONCLUSIONS

The use of brute force (a computer-intensive approach that generates large numbers of statistical tests) is an effective way to check data quality in multicenter clinical trials. It can provide a cost-effective complement to other data-management and monitoring techniques.

Cardiovascular research in France: Evolution of scientific activities and production over the last decade

BACKGROUND

Cardiovascular disease (CVD) is a major cause of death worldwide, and fruitful research is needed for future advances in this field.

AIMS

To analyse the scientific production and vitality of French cardiovascular clinical research, and its evolution over the last decade.

METHODS

We first used Lab Times online data obtained through the Web of Science (Thomson-Reuters, Toronto, ON, Canada), then the PubMed database (National Center for Biotechnology Information [NCBI], Bethesda, MD, USA), for studies published between 2005 and 2015 in the multidisciplinary and cardiology journals with the highest impact factors. French abstracts submitted and accepted at the European Society of Cardiology (ESC) congress were provided directly by the ESC. The number of cardiovascular projects was analysed through the http://www.ClinicalTrials.gov database and the French site for government-funded projects, over the decade from 2008 to 2017.

RESULTS

Overall, France was ranked fifth in Europe and eighth worldwide for CVD publications. During the 10-year period from 2005 to 2015, French publications accounted for 0.2-0.3% of articles in top multidisciplinary journals and 2% of articles in top cardiology journals. We observed a steady decrease in French abstract submissions at the ESC congress (from 5% to 3.5% in 10 years), and in 2017, France was ranked eighth in Europe. Across European countries, France has been ranked first for declared cardiovascular research on http://www.ClinicalTrials.gov over the last 3 years, for both interventional and observational studies. Regarding the Hospital Programme of Clinical Research, heart ranked second after neurosciences.

CONCLUSIONS

France is very well represented in terms of new CVD projects, but actual French scientific production scores poorly. Investing in CVD research is a priority to increase the level of publication and to compete with other leading countries.

Dexamethasone for Cardiac Surgery trial (DECS-II): Rationale and a novel, practice preference-randomized consent design

BACKGROUND

Numerous studies have investigated high-dose corticosteroids in cardiac surgery, but with mixed results leading to ongoing variations in practice around the world. DECS-II is a study comparing high-dose dexamethasone with placebo in patients undergoing cardiac surgery.

METHODS

We discuss the rationale for conducting DECS-II, a 2800-patient, pragmatic, multicenter, assessor-blinded, randomized trial in cardiac surgery, and the features of the DECS-II study design (objectives, end points, target population, based on practice preference with post-randomization consent, treatments, patient follow-up and analysis).

CONCLUSIONS

The DECS-II trial will use a novel, efficient trial design to evaluate whether high-dose dexamethasone has a patient-centered benefit of enhancing recovery and increasing the number of days at home after cardiac surgery.

Investigation of the Factors Affecting Risk-Based Quality Management of Investigator-Initiated Investigational New-Drug Trials for Unapproved Anticancer Drugs in Japan

BACKGROUND

With an increase in the complexity and cost of clinical trials and the advances in information technology, monitoring guidance issued by regulatory authorities recommends risk-adapted monitoring. To introduce the monitoring method for investigator-initiated investigational new drug (IND) trials using unapproved anticancer drugs, we performed exploratory retrospective analyses to identify risk factors for data quality.

METHODS

To select investigator-initiated IND trials using unapproved anticancer drugs, we set the trial selection criteria. Data collection was performed by using audit trails and monitoring reports. Collected data were analyzed by univariate and multivariate analyses to identify the independent risk factors related to error.

RESULTS

By trial selection criteria, 5 investigator-initiated IND trials using unapproved anticancer drugs were selected. The error rates of the total data, critical data, and noncritical data were 7.4%, 9.7%, and 5.9%, respectively. There was no difference between clinical research core hospitals certified by the Ministry of Health, Labour and Welfare and other hospitals in univariate analysis (odds ratio [OR], 1.00; 99% confidence interval [CI], 0.96-1.05; P = .9179). As the main independent risk factors related to error, critical data in the importance of data (OR, 1.28; 99% CI, 1.24-1.33; P < .0001) and groups with ≤3 patients after registration (OR, 1.12; 99% CI, 1.10-1.15; P < .0001) were significantly related to errors in multivariate analysis.

CONCLUSIONS

The results of this research suggest that the feasibility of risk-based monitoring and sampling source data verification was indicated for noncritical data and patients after the third case.

A checklist for clinical trials in rare disease: obstacles and anticipatory actions-lessons learned from the FOR-DMD trial

Background

Trials in rare diseases have many challenges, among which are the need to set up multiple sites in different countries to achieve recruitment targets and the divergent landscape of clinical trial regulations in those countries. Over the past years, there have been initiatives to facilitate the process of international study set-up, but the fruits of these deliberations require time to be operationally in place. FOR-DMD (Finding the Optimum Steroid Regimen for Duchenne Muscular Dystrophy) is an academic-led clinical trial which aims to find the optimum steroid regimen for Duchenne muscular dystrophy, funded by the National Institutes of Health (NIH) for 5 years (July 2010 to June 2015), anticipating that all sites (40 across the USA, Canada, the UK, Germany and Italy) would be open to recruitment from July 2011. However, study start-up was significantly delayed and recruitment did not start until January 2013.

Method

The FOR-DMD study is used as an example to identify systematic problems in the set-up of international, multi-centre clinical trials. The full timeline of the FOR-DMD study, from funding approval to site activation, was collated and reviewed. Systematic issues were identified and grouped into (1) study set-up, e.g. drug procurement; (2) country set-up, e.g. competent authority applications; and (3) site set-up, e.g. contracts, to identify the main causes of delay and suggest areas where anticipatory action could overcome these obstacles in future studies.

Results

Time from the first contact to site activation across countries ranged from 6 to 24 months. Reasons of delay were universal (sponsor agreement, drug procurement, budgetary constraints), country specific (complexity and diversity of regulatory processes, indemnity requirements) and site specific (contracting and approvals). The main identified obstacles included (1) issues related to drug supply, (2) NIH requirements regarding contracting with non-US sites, (3) differing regulatory requirements in the five participating countries, (4) lack of national harmonisation with contracting and the requirement to negotiate terms and contract individually with each site and (5) diversity of languages needed for study materials. Additionally, as with many academic-led studies, the FOR-DMD study did not have access to the infrastructure and expertise that a contracted research organisation could provide, organisations often employed in pharmaceutical-sponsored studies. This delay impacted recruitment, challenged the clinical relevance of the study outcomes and potentially delayed the delivery of the best treatment to patients.

Conclusion

Based on the FOR-DMD experience, and as an interim solution, we have devised a checklist of steps to not only anticipate and minimise delays in academic international trial initiation but also identify obstacles that will require a concerted effort on the part of many stakeholders to mitigate.

Electronic supplementary material

The online version of this article (10.1186/s13063-018-2645-0) contains supplementary material, which is available to authorized users.

Registry-based randomised clinical trial: efficient evaluation of generic pharmacotherapies in the contemporary era

Randomised clinical trials are the gold standard for testing the effectiveness of clinical interventions. However, increasing complexity and associated costs may limit their application in the investigation of key cardiovascular knowledge gaps such as the re-evaluation of generic pharmacotherapies. The registry-based randomised clinical trial (RRCT) leverages data sampling from nationwide quality registries to facilitate high participant inclusion rates at comparably low costs and, therefore, may offer a mechanism by which such clinical questions may be answered. To date, a number of studies have been conducted using such trial designs, but uncritical use of the RRCT design may lead to erroneous conclusions. The current review provides insights into the strengths and weaknesses of the RRCT, as well as provides an exploratory example of how a trial may be designed to test the long-term effectiveness of beta blockers in patients with myocardial infarction who have preserved left ventricular systolic function.

What are the main inefficiencies in trial conduct: a survey of UKCRC registered clinical trials units in the UK

BACKGROUND

The UK Clinical Research Collaboration (UKCRC) registered Clinical Trials Units (CTUs) Network aims to support high-quality, efficient and sustainable clinical trials research in the UK. To better understand the challenges in efficient trial conduct, and to help prioritise tackling these challenges, we surveyed CTU staff. The aim was to identify important inefficiencies during two key stages of the trial conduct life cycle: (i) from grant award to first participant, (ii) from first participant to reporting of final results.

METHODS

Respondents were asked to list their top three inefficiencies from grant award to recruitment of the first participant, and from recruitment of the first participant to publication of results. Free text space allowed respondents to explain why they thought these were important. The survey was constructed using SurveyMonkey and circulated to the 45 registered CTUs in May 2013. Respondents were asked to name their unit and job title, but were otherwise anonymous. Free-text responses were coded into broad categories.

RESULTS

There were 43 respondents from 25 CTUs. The top inefficiency between grant award and recruitment of first participant was reported as obtaining research and development (R&D) approvals by 23 respondents (53%), contracts by 22 (51%), and other approvals by 13 (30%). The top inefficiency from recruitment of first participant to publication of results was failure to meet recruitment targets, reported by 19 (44%) respondents. A common comment was that this reflected overoptimistic or inaccurate estimates of recruitment at site. Data management, including case report form design and delays in resolving data queries with sites, was reported as an important inefficiency by 11 (26%) respondents, and preparation and submission for publication by 9 (21%).

CONCLUSIONS

Recommendations for improving the efficiency of trial conduct within the CTUs network include: further reducing unnecessary bureaucracy in approvals and contracting; improving training for site staff; realistic recruitment targets and appropriate feasibility; developing training across the network; improving the working relationships between chief investigators and units; encouraging funders to release sufficient funding to allow prompt recruitment of trial staff; and encouraging more research into how to improve the efficiency and quality of trial conduct.

Global geographical variation in patient characteristics in percutaneous coronary intervention clinical trials: A systematic review and meta-analysis

BACKGROUND

We sought to determine whether there are differences in enrolled patients' risk factors in published percutaneous coronary intervention (PCI) trials between various continents.

METHODS

We systematically identified clinical trials evaluating PCI interventions through PubMed. We reviewed 701 studies between 1990 and 2014 from North America (N=135), Europe (N=403), and Asia (N=163), examining the prevalence of cardiovascular risk factors-hypertension (HTN), diabetes mellitus (DM), hyperlipidemia (HL), smoking, sex, and body mass index. We performed meta-regression with random- and mixed-effects models to compare patient baseline characteristics between continents and linear meta-regression analysis to test trends over time.

RESULTS

In meta-regression with random-effects model, North American trials recruited the lowest proportion of male participants (71.32%), followed by Asian (74.41%) and European trials (76.47%; P<.0001). North American trials enrolled the highest proportion of patients with HTN (63.17%, P=.0035) and HL (63.72%, P<.0001), whereas Asia enrolled the highest proportion of DM patients (29.64%, P<.0001) and smoking (38.41%, P=.0144). When adjusting for other moderators such as publication date, body mass index, and sex in meta-regression with mixed-effects model, age was significantly positively correlated with HTN, HL, DM, and smoking (P<.001). Body mass index was significantly higher in Europe and North America than in Asia. All enrollment risk factors demonstrated (β<0.02) statistically significant temporal trends over time, except for sex.

CONCLUSIONS

There are major continental differences in risk factors among patients enrolled in PCI trials from various continents. Clinical trial results may not be applicable to patient populations from another region.